Super‐resolution of brain tumor MRI images based on deep learning

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Abstract

Introduction

To explore and evaluate the performance of MRI‐based brain tumor super‐resolution generative adversarial network (MRBT‐SR‐GAN) for improving the MRI image resolution in brain tumors.

Methods

A total of 237 patients from December 2018 and April 2020 with T2‐fluid attenuated inversion recovery (FLAIR) MR images (one image per patient) were included in the present research to form the super‐resolution MR dataset. The MRBT‐SR‐GAN was modified from the enhanced super‐resolution generative adversarial networks (ESRGAN) architecture, which could effectively recover high‐resolution MRI images while retaining the quality of the images. The T2‐FLAIR images from the brain tumor segmentation (BRATS) dataset were used to evaluate the performance of MRBT‐SR‐GAN contributed to the BRATS task.

Results

The super‐resolution T2‐FLAIR images yielded a 0.062 dice ratio improvement from 0.724 to 0.786 compared with the original low‐resolution T2‐FLAIR images, indicating the robustness of MRBT‐SR‐GAN in providing more substantial supervision for intensity consistency and texture recovery of the MRI images. The MRBT‐SR‐GAN was also modified and generalized to perform slice interpolation and other tasks.

Conclusions

MRBT‐SR‐GAN exhibited great potential in the early detection and accurate evaluation of the recurrence and prognosis of brain tumors, which could be employed in brain tumor surgery planning and navigation. In addition, this technique renders precise radiotherapy possible. The design paradigm of the MRBT‐SR‐GAN neural network may be applied for medical image super‐resolution in other diseases with different modalities as well.

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Author and article information

Contributors

Yifeng Miao: yifengmwx@hotmail.com

Yongming Qiu: yongmingqiu356@163.com

Journal

Journal ID (nlm-ta): J Appl Clin Med Phys

Journal ID (iso-abbrev): J Appl Clin Med Phys

Journal ID (doi): 10.1002/(ISSN)1526-9914

Journal ID (publisher-id): ACM2

Title: Journal of Applied Clinical Medical Physics

Publisher: John Wiley and Sons Inc. (Hoboken )

ISSN (Electronic): 1526-9914

Publication date (Electronic): 15 September 2022

Publication date Collection: November 2022

Volume: 23

Issue: 11 ( doiID: 10.1002/acm2.v23.11 )

Electronic Location Identifier: e13758

Affiliations

[ ¹ ] Brain Injury Center, Department of Neurosurgery Renji Hospital, School of Medicine, Shanghai Jiao Tong University Shanghai China

[ ² ] Shanghai Xunshi Technology Co., Ltd. Shanghai China

[ ³ ] School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China

[ ⁴ ] Department of Neurosurgery Renji Hospital, School of Medicine Shanghai Jiao Tong University Shanghai China

Author notes

[*] [* ] Correspondence

Yifeng Miao and Yongming Qiu, Renji Hospital, No. 160, Pujian Road, Pudong New District, Shanghai, China.

Email: yifengmwx@ 123456hotmail.com and yongmingqiu356@ 123456163.com

Article

Publisher ID: ACM213758

DOI: 10.1002/acm2.13758

PMC ID: 9680577

PubMed ID: 36107021

SO-VID: c8e09173-6454-49ab-9d2d-49bef6bf3da9

License:

This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

History

Date revision received : 07 June 2022

Date received : 24 November 2021

Date accepted : 02 August 2022

Page count

Figures: 10, Tables: 4, Pages: 13, Words: 6824

Funding

Funded by: Scientific Research Project of Shanghai Minhang District Health and Family Planning Commission

Award ID: 2018MW57

Custom metadata

source-schema-version-number 2.0

cover-date November 2022

details-of-publishers-convertor Converter:WILEY_ML3GV2_TO_JATSPMC version:6.2.1 mode:remove_FC converted:22.11.2022

Keywords: brain tumor,generative adversarial network,magnetic resonance imaging,super‐resolution

Data availability:

Keywords: brain tumor, generative adversarial network, magnetic resonance imaging, super‐resolution